Identifying ECG signals having the same morphology

ABSTRACT

Acquiring ECG signals from electrodes positioned in a heart taken over a single heartbeat, selecting a morphology pattern within a window of interest around time of occurrence annotations for the signals; computing a weighted cross-correlation between each morphology pattern of the signals and a stored template morphology pattern, to generate a weighted correlation coefficient of a match between the morphology patterns of the acquired signals and the stored morphology pattern; iteratively changing a phase of the signals relative to the phase of the morphology pattern and repeating the step of generating the weighted correlation coefficient at each iteration; determining a maximum value of the weighted correlation coefficient based on the iterations; comparing the maximum value to a threshold; and when the maximum value exceeds the threshold, accepting the heartbeat as having been caused by the arrhythmia and incorporating a location of the arrhythmia into a local activation map.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation application of U.S. Ser. No.15/646,344 filed on Jul. 11, 2017 and claims the benefit of U.S.Provisional Patent Application 62/372,969, filed Aug. 10, 2016, whichare incorporated herein by reference.

FIELD OF THE INVENTION

This invention relates generally to electrocardiograph (ECG) signals,and specifically to detecting ECG signals having similar morphologies.

BACKGROUND OF THE INVENTION

For correctly mapping regions of a heart chamber which generate anarrhythmia, it is essential that only signals, or beats, exhibiting thatspecific arrhythmia are captured. Signals from effects such as ectopicbeats, mechanical stimulation of the tissue, and arrhythmia changes inmorphology due to alternative activation patterns with the same cyclelength, should be ignored. Introducing results from such signals into amap will cause inaccuracies in the local activation map, and thedeformed visualization of the arrhythmia makes it difficult to clearlyidentify the arrhythmia mechanisms.

Documents incorporated by reference in the present patent applicationare to be considered an integral part of the application except that, tothe extent that any terms are defined in these incorporated documents ina manner that conflicts with definitions made explicitly or implicitlyin the present specification, only the definitions in the presentspecification should be considered.

SUMMARY OF THE INVENTION

An embodiment of the present invention provides a method, including:

acquiring a set of electrocardiograph (ECG) signals from a set ofelectrodes positioned in a heart of a human subject taken over a singleheartbeat, the set of ECG signals having time of occurrence annotationsfor an activation of an arrhythmia;

selecting a morphology pattern within a window of interest around thetime of occurrence annotations for set of ECG signals;

computing a weighted cross-correlation between each morphology patternof the set of ECG signals and a stored template morphology patternwithin the window of interest, so as to generate a weighted correlationcoefficient that is a measure of a match between the morphology patternsof the acquired set of ECG signals and the stored morphology pattern;

iteratively changing a phase of the set of ECG signals relative to thephase of the morphology pattern and repeating the step of generating theweighted correlation coefficient at each iteration;

determining a maximum value of the weighted correlation coefficientbased on the iterations;

comparing the maximum value of the weighted correlation coefficient to athreshold coefficient; and

when the maximum value of the weighted correlation coefficient exceedsthe threshold coefficient, accepting the heartbeat as having been causedby the arrhythmia and automatically incorporating an indication of alocation of the arrhythmia into a local activation map of the heart ofthe human subject.

A disclosed embodiment includes calculating an absolute maximumamplitude of the set of ECG signals, an absolute maximum amplitude ofthe selected morphology pattern of the set of ECG signals and using thesum of the absolute maximum amplitude of the set of ECG signals and theabsolute maximum amplitude of the selected morphology pattern of the setof ECG signals as weights for generating the weighted correlationcoefficient.

A further disclosed embodiment includes computing a first summation ofsample values of the ECG data of the template morphology pattern less anaverage value of the ECG data of the template morphology pattern,multiplied by sample values of the ECG data of the acquired ECG signalless an average value of the ECG data of the acquired ECG signal. Afurther disclosed embodiment includes computing a second summation of asquare of the sample values of the ECG data of the template morphologypattern less the average value of the ECG data of the templatemorphology pattern, multiplied by a square of the sample values of theECG data of the acquired ECG signal less the average value of the ECGdata of the acquired ECG signal. A further disclosed embodiment includescomputing the weighted cross-correlation from the first summationdivided by the square root of the second summation.

The ECG signals may be body surface (BS) ECG signals.

Alternatively or additionally, the ECG signals may be intra-cardiac (IC)ECG signals.

There is further provided, according to an embodiment of the presentinvention, apparatus, including:

a set of electrodes, configured to receive an initial set ofelectrocardiograph (ECG) signals taken over a single heartbeat of ahuman subject, the set having respective morphologies to be used as atemplate for an arrhythmia of the subject, and to receive a subsequentset of ECG signals taken over a subsequent heartbeat of the humansubject; and

a processor, configured to perform a cross-correlation between theinitial set and the subsequent set, so as to generate a correlationcoefficient that is a measure of a goodness of fit between geometries ofthe initial set and the subsequent set, and when the correlationcoefficient exceeds a threshold coefficient, accept the subsequentheartbeat as having been caused by the arrhythmia.

The present disclosure will be more fully understood from the followingdetailed description of the embodiments thereof, taken together with thedrawings, in which:

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic illustration of an invasive medical procedure,according to an embodiment of the present invention;

FIG. 2 is a schematic block diagram illustrating inputs and outputs ofan ECG morphology matching algorithm, according to an embodiment of thepresent invention;

FIG. 3 is a schematic block diagram illustrating the ECG morphologymatching algorithm, according to an embodiment of the present invention;and

FIGS. 4, 5, and 6 are schematic diagrams illustrating operations ofblocks of the algorithm, according to an embodiment of the presentinvention.

DETAILED DESCRIPTION OF EMBODIMENTS Overview

Embodiments of the present invention provide an ECG morphology matchingalgorithm which aims to identify all beats representing the samemorphology in ECG signals. The algorithm is compatible with body surface(BS) (typically 12 leads) signals and/or intra-cardiac (IC) signals. Thealgorithm receives a morphology pattern of ECG signals as an input andsearches for the same morphology in continuous ECG signals.

A user of the present invention selects the input morphology pattern bydefining a window of interest (WOI) around a specific annotation. Thealgorithm compares the morphology of the selected pattern with themorphology of incoming ECG signals. Beats that are within apredetermined weighted correlation threshold are considered to representthe same morphology. The algorithm operates in real-time, as beatsignals are acquired by a probe in the heart.

By using the results of the algorithm, regions of the heart that are thesource of the matched beats may be indicated automatically on a map ofthe heart.

An embodiment of the present invention provides a method, comprisingselecting an initial set of electrocardiograph (ECG) signals taken overa single heartbeat of a human subject, the set having respectivemorphologies to be used as a template for an arrhythmia of the subject,and receiving a subsequent set of ECG signals taken over a subsequentheartbeat of the human subject. The method further comprises performinga cross-correlation between the initial set and the subsequent set, soas to generate a correlation coefficient that is a measure of a goodnessof fit between geometries of the initial set and the subsequent set.When the correlation coefficient exceeds a threshold coefficient, thesubsequent heartbeat is accepted as having been caused by thearrhythmia.

DESCRIPTION OF EMBODIMENTS

FIG. 1 is a schematic illustration of an invasive medical procedureusing an apparatus 20, according to an embodiment of the presentinvention. The procedure is performed by a medical professional 22, and,by way of example, the procedure in the description herein below isassumed to comprise acquisition of intra-cardiac electrocardiogram (ICECG) signals from a heart 24 of a human patient 26. While embodiments ofthe present invention analyze either IC ECG or BS (body surface) ECGsignals, for simplicity and clarity the following description, exceptwhere otherwise stated, assumes that IC ECG signals are analyzed.

In order to acquire the IC ECG signals, professional 22 inserts a probe28 into a sheath 30 that has been pre-positioned in a lumen of thepatient. Sheath 30 is positioned so that a distal end 32 of the probemay enter the heart of the patient, after exiting a distal end 34 of thesheath, and contact tissue of the heart.

Probe 28 may comprise any type of catheter that can be inserted into theheart of the patient, and that can be tracked, typically using amagnetic tracking system and/or an impedance measuring system. Forexample, probe 28 may comprise a lasso catheter, a shaft-like catheter,or a pentaRay catheter, produced by Biosense Webster of Diamond Bar,Calif., or catheters generally similar to these catheters. BiosenseWebster also produces a magnetic tracking system and an impedancemeasuring system that may be used in embodiments of the presentinvention.

Probe 28 comprises one or more electrodes 36, which are used to acquirethe ECG signals used by a processor 40, comprised in apparatus 20, inperforming the algorithms described herein. Processor 40, in addition toacting as a central processing unit, may comprise real-time noisereduction circuitry 44, typically configured as a field programmablegate array (FPGA), followed by an analog-to-digital (A/D) signalconversion integrated circuit 46. The processor can pass the signal fromA/D circuit 46 to another processor and can be programmed to perform thealgorithms disclosed herein.

Processor 40 is located in an operating console 60 of the apparatus.Console 60 comprises controls 62 which are used by professional 22 tocommunicate with the processor. During the procedure, processor 40communicates with an ECG module 66 in a module bank 70, in order toacquire ECG signals as well as to perform the algorithms disclosedherein.

ECG module 66 receives ECG signals from electrode 36. In one embodimentthe signals are transferred, in module 66, through a low noisepre-amplifier 68, and via low pass and high pass filters 71A, 71B, to amain amplifier 72. Module 436 also comprises an analog to digitalconverter (ADC) 74, which transfers digitized values of the ECG signalsto processor 40, for implementation by the processor of the algorithmsdescribed herein. Typically, processor 40 controls the operation ofpre-amplifier 68, filters 71A, 71B, amplifier 72, and ADC 74.

For simplicity FIG. 1 illustrates ECG module 66 as having one channelfor receiving signals from electrode 36. However, it will be understoodthat the module typically comprises multiple channels substantiallysimilar to that shown. For example, module 66 may comprise 12 suchchannels, which may be used to receive signals from 12 body surfaceelectrodes.

ECG module 66 enables processor 40 to acquire and analyze EP(electrophysiological) signals received by electrode 36, including theECG signals referred to herein. The signals are typically presented toprofessional 22 as voltage-time graphs, which are updated in real time,on a display screen 80.

The software for processor 40 and module bank 70 may be downloaded tothe processor in electronic form, over a network, for example.Alternatively or additionally, the software may be provided onnon-transitory tangible media, such as optical, magnetic, or electronicstorage media.

In order to operate apparatus 20, module bank 70 typically comprisesmodules other than the ECG module described above, such as one or moretracking modules allowing the processor to track the distal end of probe28. For simplicity, such other modules are not illustrated in FIG. 1.All modules may comprise hardware as well as software elements.

In addition to display screen 80 presenting ECG signals acquired byelectrode 411, results of the algorithms described herein may also bepresented to the algorithm user on the display screen. For example, theresults may be incorporated into a map 82 of heart 24.

FIG. 2 is a schematic block diagram illustrating inputs and outputs ofan ECG morphology matching algorithm, according to an embodiment of thepresent invention. The algorithm is implemented by processor 40, andacts as a morphology matching filter 100 which accepts as inputs:

-   -   A set 102 of ECG signals which have been processed as described        above, by multiple channels of ECG module 66.    -   Reference annotations 104 of the signals, computed by processor        40. An annotation of a signal is an assumed time of occurrence        of the signal. In one embodiment the annotation corresponds to        the time of occurrence of the largest positive value on one        selected ECG signal. Several criteria options exist for the        reference annotation (positive value, negative value, largest        negative slope, and largest positive slope) and for IC ECG        signals the time of occurrence typically corresponds to the time        of activation of the section of myocardium generating the        signal. Criteria for choosing the ECG signal for annotations,        corresponding to that described above or other criteria may be        defined by professional 22. The professional also selects the        ECG channels to be used to acquire the signals being analyzed.        For BS ECG signals there are typically 12 channels; for IC ECG        signals the number of channels corresponds to the number of        electrodes 36 being used.    -   A morphology pattern 106. This is a set of ECG signals that is        selected by professional 22, and that is captured at a specific        point in time, with a window of interest (WOI) around        annotations of the signals that are defined by the professional.        The WOI defines the time period for the morphology matching        algorithm. Morphology pattern 106 acts as a template against        which other ECG signals are compared, and the pattern may also        be referred to herein as a template.    -   A correlation threshold 108, set by professional 22, to be used        by the algorithm in deciding if a beat matches the morphology        pattern.

Outputs of the algorithm are:

-   -   A beat status 110. I.e., an accepted beat having the same        morphology as the input morphology pattern 106, or a rejected        beat having a different morphology from the input pattern. In        one embodiment a location of an accepted beat is incorporated        into a map of the heart generating the ECG signals.    -   A correlation score 112 for each channel of set 102 of signals        (in some embodiments these scores may not be presented to        professional 22).    -   A weighted correlation score 114, calculated over all the        channels, for each beat.

FIG. 3 is a schematic block diagram illustrating the ECG morphologymatching algorithm, and FIGS. 4, 5, and 6 are schematic diagramsillustrating operations of blocks of the algorithm, according to anembodiment of the present invention. By way of example processor 40 isassumed to operate the algorithm. In other embodiments the processor maybe a stand-alone processor, and/or a general purpose processor that istypically operating a computer.

In a first step of the algorithm, corresponding to the “single ChannelCorrelation” block 120, the processor performs a correlation, withstored morphology pattern 106, within the WOI period as defined byprofessional 22, for every channel of an incoming beat. FIG. 4illustrates how the correlation is performed. As shown in FIG. 4, theprocessor uses as inputs:

-   -   Morphology pattern 106 (Pattern described above with reference        to FIG. 2.    -   An ECG signal to be tested (ECG i,j). The signal to be tested        has a WOI temporal width corresponding to the WOI width of the        morphology pattern defined by professional 22. The temporal        position of the WOI is selected to include a real-time        annotation, calculated by the processor, of the signal.

i is a numerical index defining the channel of the pattern (typically,for BS ECG, i=1, 2, . . . 12), and j is a numerical index defining aposition of an annotation of the ECG signal.

The processor calculates, for each channel, a correlation coefficientaccording to the following equation:

$\begin{matrix}{{{Correlation}\mspace{11mu}\left( {x,y} \right)} = \frac{\sum\limits_{k}\;{\left( {x - \overset{\_}{x}} \right)\left( {y - \overset{\_}{y}} \right)}}{\sqrt{\sum\limits_{k}\;{\left( {x - \overset{\_}{x}} \right)^{2}\left( {y - \overset{\_}{y}} \right)^{2}}}}} & (1)\end{matrix}$

where

x is the sample value of the template reference ECG data,

x is the average value of the template reference ECG data,

y is the sample value of the current beat ECG data being tested,

y is the average value of the current beat ECG data being tested, and

k is a numerical index defining which data sample of the ECG signal isbeing analyzed. For example, if the WOI is for 120 msec, from −50 msec(before the reference annotation) to +70 msec (after the referenceannotation), and we sample every msec, then k is a set of 120 values forthe 120 samples.

It will be understood that the correlation performed by equation (1)compares the geometries, or shapes, of the template reference ECG datawith the current beat ECG data. A high value of Correlation (x,y), i.e.,close to unity, means that the two geometries, of the template and ofthe current beat, are similar.

In a second step of the algorithm, corresponding to an “Overall WeightedCorrelation” block 124 of FIG. 3, the processor calculates an overallcorrelation, for a specific beat, using the values of the correlationcoefficient calculated in the first step, i.e., according to equation(1).

FIG. 5 illustrates how the correlation is performed. As shown in FIG. 5,the processor uses as inputs:

-   -   The correlation score, i.e., the output of equation (1) for each        channel of a beat being tested. The beat being tested is the ECG        signal (of the particular channel) which is in a WOI around a        current annotation.    -   The ECG signal (beat) being tested.    -   The morphology pattern (Pattern described above with reference        to FIG. 1.

Also as shown in FIG. 5, the processor calculates an absolute maximumamplitude Ai,j of the ECG signal being tested, and an absolute maximumamplitude Bi of the morphology pattern.

The processor uses the sum of Ai,j and Bi as weights to calculate anoverall correlation according to equation (2):

$\begin{matrix}{{{Overall}\mspace{14mu}{Correlation}} = \frac{\sum\limits_{i = 1}^{N}\;{\left( {A_{i,j} + B_{i}} \right){Corr}_{i,j}}}{\sum\limits_{i = 1}^{N}\;\left( {A_{i,j} + B_{i}} \right)}} & (2)\end{matrix}$

Where Corri,j is the correlation coefficient calculated by equation (1),and N is the number of ECG channels being analyzed. In the case of BSsignals, N is typically 12.

The overall correlation coefficient calculated by equation (2) dependson the phase of the ECG signal being tested relative to the phase of themorphology pattern.

In a third step of the algorithm, corresponding to a “Phase Shift” block128 of FIG. 3, the processor iteratively changes the phase, of the ECGsignal being tested, relative to the phase of the morphology pattern.The processor uses as inputs:

-   -   The value of the overall correlation from equation (2).    -   The correlation threshold 108 (FIG. 2) as set by professional        22. The threshold may be between 0 and 1, and a typical value is        0.9.

FIG. 6 explains, in an iteration set of blocks, the iterative processperformed by single channel correlation block 120, overall weightedcorrelation block 124, and phase shift block 128. As shown in FIG. 6, ateach iteration, the processor repeats the first two steps describedabove, in a “shifted single channel correlation” block 120′ and a“shifted overall correlation” Block 124′. During the iterations theprocessor determines a maximum value of the overall correlation as theresult of equation (2).

FIG. 6 illustrates that phase shift iterations are performed every 1msec in a ±40 msec time frame measured from the annotation of the beatbeing analyzed. An index k defines the phase shift being evaluated, andk={−40, . . . 0, . . . +40}. At each iteration the value of the shiftedoverall correlation is compared to a previous maximum correlation in acomparison block 130, and if comparison 130 returns positive the overallcorrelation is updated in an update block 134.

If the return is negative control continues to a comparison block 138,which checks if there are any more values of index k to be iterated. Ifthere are, k is incremented in an incremental block 142, the new valueof k is applied to the ECG signal in a signal block 146, and theflowchart returns to block 120′.

If the iterations have completed, then control continues to a finalcomparison block 152, where the output of the iteration set of blocks,the maximum value of the overall correlation that is in block 134 iscompared to the input threshold value. If the comparison returnspositive, the beat is assumed to represent the same arrhythmia as themorphology pattern. In this case processor 40 may add this beatinformation into collective information of map 82 of the heart (FIG. 1).If the comparison returns negative, the beat is assumed to represent adifferent arrhythmia from the morphology pattern, and therefore theinformation is not added to the map collective information.

It will be appreciated that the embodiments described above are cited byway of example, and that the present invention is not limited to whathas been particularly shown and described hereinabove. Rather, the scopeof the present invention includes both combinations and subcombinationsof the various features described hereinabove, as well as variations andmodifications thereof which would occur to persons skilled in the artupon reading the foregoing description and which are not disclosed inthe prior art.

The invention claimed is:
 1. A computer implemented method, comprising:acquiring a set of electrocardiograph (ECG) signals from a set ofelectrodes positioned in a heart of a human subject taken over a singleheartbeat, the set of ECG signals having time of occurrence annotationsfor an activation of an arrhythmia, the set of ECG signals having a setof respective morphology patterns; selecting a morphology pattern of theplurality of morphology patterns within a window of interest around eachof the time of occurrence annotations for the set of ECG signals;computing a weighted cross-correlation between each of the selectedmorphology patterns of the set of ECG signals and a stored templatemorphology pattern within the window of interest, and generating aweighted correlation coefficient that is a measure of a match betweenthe selected morphology patterns of the acquired set of ECG signals andthe stored template morphology pattern; iteratively changing a phase ofthe set of ECG signals relative to the phase of the selected morphologypatterns and repeating the step of generating the weighted correlationcoefficient at each iteration; determining a maximum value of theweighted correlation coefficient based on the iterations; comparing themaximum value of the weighted correlation coefficient to a thresholdcoefficient; and when the maximum value of the weighted correlationcoefficient exceeds the threshold coefficient, accepting the heartbeatas having been caused by the arrhythmia and automatically incorporatingan indication of a location of the arrhythmia into a local activationmap of the heart of the human subject.
 2. The method according to claim1, further comprising calculating an absolute maximum amplitude of theset of ECG signals, an absolute maximum amplitude of the selectedmorphology pattern of the set of ECG signals and using a sum of theabsolute maximum amplitude of the set of ECG signals and the absolutemaximum amplitude of the selected morphology pattern of the set of ECGsignals as weights for generating the weighted correlation coefficient.3. The method according to claim 1, wherein computing the weightedcross-correlation includes a first summation of: sample values of theECG data of the template morphology pattern less an average value of theECG data of the template morphology pattern, multiplied by sample valuesof the ECG data of the acquired ECG signal less an average value of theECG data of the acquired ECG signal.
 4. The method according to claim 3,wherein computing the weighted cross-correlation includes a secondsummation of: a square of the sample values of the ECG data of thetemplate morphology pattern less the average value of the ECG data ofthe template morphology pattern, multiplied by a square of the samplevalues of the ECG data of the acquired ECG signal less the average valueof the ECG data of the acquired ECG signal.
 5. The method according toclaim 4, wherein computing the weighted cross-correlation includes thefirst summation divided by the square root of the second summation. 6.The method according to claim 1, wherein the ECG signals are bodysurface (BS) ECG signals.
 7. The method according to claim 1, whereinthe ECG signals are intra-cardiac (IC) ECG signals.
 8. An apparatus,comprising: a set of electrodes, configured to be positioned in a heartof a human subject for acquiring a set of electrocardiograph (ECG)signals taken over a single heartbeat, the set of ECG signals havingtime of occurrence annotations for an activation of an arrhythmia, theset of ECG signals having a set of respective morphology patterns; and aprocessor, configured for: selecting a morphology pattern of theplurality of morphology patterns within a window of interest around eachof the time of occurrence annotations for the set of ECG signals;computing a weighted cross-correlation between each of the selectedmorphology patterns of the set of ECG signals and a stored templatemorphology pattern within the window of interest, and generating aweighted correlation coefficient that is a measure of a match betweenthe selected morphology patterns of the acquired set of ECG signals andthe stored template morphology pattern; iteratively changing a phase ofthe set of ECG signals relative to the phase of the selected morphologypatterns and repeating the step of generating the weighted correlationcoefficient at each iteration; determining a maximum value of theweighted correlation coefficient based on the iterations; comparing themaximum value of the weighted correlation coefficient to a thresholdcoefficient; and when the maximum value of the weighted correlationcoefficient exceeds the threshold coefficient, accepting the heartbeatas having been caused by the arrhythmia and automatically incorporatingan indication of a location of the arrhythmia into a local activationmap of the heart of the human subject.
 9. The apparatus according toclaim 8, further comprising calculating an absolute maximum amplitude ofthe set of ECG signals, an absolute maximum amplitude of the selectedmorphology pattern of the set of ECG signals and using a sum of theabsolute maximum amplitude of the set of ECG signals and the absolutemaximum amplitude of the selected morphology pattern of the set of ECGsignals as weights for generating the weighted correlation coefficient.10. The apparatus according to claim 8, wherein computing the weightedcross-correlation includes a first summation of: sample values of theECG data of the template morphology pattern less an average value of theECG data of the template morphology pattern, multiplied by sample valuesof the ECG data of the acquired ECG signal less an average value of theECG data of the acquired ECG signal.
 11. The apparatus according toclaim 10, wherein computing the weighted cross-correlation includes asecond summation of: a square of the sample values of the ECG data ofthe template morphology pattern less the average value of the ECG dataof the template morphology pattern, multiplied by a square of the samplevalues of the ECG data of the acquired ECG signal less the average valueof the ECG data of the acquired ECG signal.
 12. The apparatus accordingto claim 11, wherein computing the weighted cross-correlation includesthe first summation divided by the square root of the second summation.13. The apparatus according to claim 8, wherein the ECG signals are bodysurface (BS) ECG signals.
 14. The apparatus according to claim 8,wherein the ECG signals are intra-cardiac (IC) ECG signals.